07_peakMatrix_processing
load signac RDS object:
signac<-readRDS(paste0(projPath,"/H3K27ac_BinClusteredObject.RDS"))
#
# metadata<-readRDS(paste0(projPath, "metadataObject.RDS"))
annotations<-readRDS("/scratch/kristian/scATAC/signac/EnsDb.Mmusculus.v79_getRangesAnnotation.RDS")Read in peak lists from MACS2:
MACS2: pseudobulk broadpeak:
broadpeak<-read.table(paste0(projPath, "/macs2/BinClusters/broad/",ExptName,"_", ExptName,"_AllCellrangerFragments_peaks.broadPeak"), header = FALSE,stringsAsFactors=FALSE)
macs2Pseudobulk<-remove_blacklisted2Granges(peakTable=broadpeak)##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )signac$FrIP_macs2Pseudobulk<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = macs2Pseudobulk
)
hist(signac$FrIP_macs2Pseudobulk, breaks=100)
VlnPlot(signac, features ="FrIP_macs2Pseudobulk" )
MACS2: Per BinCluster
files<-
list.files(path=paste0(projPath, "/macs2/BinClusters/broad"),pattern =paste0(ExptName, "_BinCluster"), full.names = T)
files<-files[grep(pattern="_peaks.broadPeak",x=files)]
data <- files %>%
# read in all the files, appending the path before the filename
map(~ read.table( ., header = FALSE,stringsAsFactors=FALSE))
# binCluster_GR_white<-resize(binCluster_GR_white,width = 1000)
#############################
#### Collect all in one file:
#############################
binClustersDFlist<-lapply(data, function(x) remove_blacklisted2Granges(peakTable=x))##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )binClustersList<-GRangesList(binClustersDFlist)
macs2BinClusters<-unlist(reduce(binClustersList))
rtracklayer::export.bed(object = macs2BinClusters,paste0(projPath,"/macs2/BinClusters/broad/",ExptName,"_macs2BinClusters.bed"))
#plotting:
signac$FrIP_macs2BinClusters<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = macs2BinClusters
)
hist(signac$FrIP_macs2BinClusters, breaks=100)
VlnPlot(signac, features ="FrIP_macs2BinClusters" )
Cellranger pseudobulk peaks:
cellrangerPeaks<-read.table(paste0(outsPath,"/peaks.bed"), header = FALSE,stringsAsFactors=FALSE)
cellrangerPseudobulk<-bed3_2white_Granges(peakTable=cellrangerPeaks)##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )signac$FrIP_cellrangerPseudobulk<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = cellrangerPseudobulk
)
hist(signac$FrIP_cellrangerPseudobulk, breaks=100)
VlnPlot(signac, features ="FrIP_cellrangerPseudobulk" )
SEACR pseudobulk peaks:
seacrPeaks<-read.table(paste0(projPath,"/seacr/",ExptName,"_FilteredCells_SEACRpeaks_stringentTop0.01peaks.bed.stringent.bed"),
header = FALSE,stringsAsFactors=FALSE)
seacrPseudobulk<-bed3_2white_Granges(peakTable=cellrangerPeaks)##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )signac$FrIP_seacrPseudobulk<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = seacrPseudobulk
)
hist(signac$FrIP_seacrPseudobulk, breaks=100)
VlnPlot(signac, features ="FrIP_seacrPseudobulk" )
SEACR: Per BinCluster
files<-
list.files(path=paste0(projPath, "/seacr"),pattern =paste0( "BinCluster"), full.names = T)
files<-files[grep(pattern="SEACRpeaks_stringentTop0.01peaks.bed.stringent.bed",x=files)]
data <- files %>%
# read in all the files, appending the path before the filename
map(~ read.table( ., header = FALSE,stringsAsFactors=FALSE))
seacrBinClustersList<-lapply(data, function(x) bed6_ToWhite_GRanges(peakTable=x))##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )
##
##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )seacrBinClustersGRList<-GRangesList(seacrBinClustersList)
seacrBinClusters<-unlist(reduce(seacrBinClustersGRList))
rtracklayer::export.bed(object = seacrBinClusters,paste0(projPath,"/seacr/",ExptName,"_seacrBinClusters_AllInOne.bed"))
signac$FrIP_seacrBinClusters<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = seacrBinClusters
)
hist(signac$FrIP_seacrBinClusters, breaks=100)
VlnPlot(signac, features ="FrIP_seacrBinClusters" )
ChipSeeker annotation of peaks
# binAnno <- annotatePeak(binClusters, TxDb = TxDb.Mmusculus.UCSC.mm10.knownGene::TxDb.Mmusculus.UCSC.mm10.knownGene,level = "gene", , annoDb="org.Mm.eg.db")
#
# vennpie(binAnno)
# plotAnnoPie(binAnno)
# upsetplot(binAnno)
#list of anno
filelist<- list(
"cellrangerPseudobulk"=cellrangerPseudobulk,
"macs2Pseudobulk"=macs2Pseudobulk,
"seacrPseudobulk"=seacrPseudobulk,
"macs2BinClusters"=macs2BinClusters,
"seacrBinClusters"=seacrBinClusters
)
peakAnnoList <- lapply(filelist, annotatePeak, TxDb=TxDb.Mmusculus.UCSC.mm10.knownGene::TxDb.Mmusculus.UCSC.mm10.knownGene,
tssRegion=c(-3000, 3000), verbose=FALSE)
names(peakAnnoList)<-names(filelist)
plotDistToTSS(peakAnnoList)
# genes = lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
# names(genes) = sub("_", "\n", names(genes))
# compKEGG <- clusterProfiler::compareCluster(geneClusters = genes,
# fun = "enrichKEGG",
# pvalueCutoff = 0.05,
# pAdjustMethod = "BH")
# dotplot(compKEGG, showCategory = 15, title = "KEGG Pathway Enrichment Analysis")
genes= lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
vennplot(genes)
### Choose which peak finding method for downstream analysis:
binClusters<-macs2BinClusters
#binClusters<-seacrBinClusters
signac$FrIP_binClusters<-FractionCountsInRegion(
object = signac,
assay = 'bin5000',
regions = binClusters
)
hist(signac$FrIP_binClusters, breaks=100)
VlnPlot(signac, features ="FrIP_binClusters" )
Create signac object from peak list
fragments <- CreateFragmentObject(
path = paste0(outsPath, "/fragments.tsv.gz"),
cells=rownames(signac@meta.data),
validate.fragments = FALSE
)## Computing hash#add new counts based on MACS2 peaks:
counts <- FeatureMatrix(
fragments =fragments,
features = binClusters,
process_n = 50,
cells=rownames(signac@meta.data)
)## Extracting reads overlapping genomic regionsdim(counts)## [1] 68396 636signac[["peaks"]] <- CreateChromatinAssay(counts = counts,
fragments = fragments@path,
annotation = annotations
)## Warning in CreateChromatinAssay(counts = counts, fragments = fragments@path, :
## Overlapping ranges supplied. Ranges should be non-overlapping.## Warning: Non-unique features (rownames) present in the input matrix, making
## unique## Computing hashDefaultAssay(signac) <- 'peaks'
signac## An object of class Seurat
## 454368 features across 636 samples within 3 assays
## Active assay: peaks (68396 features, 0 variable features)
## 2 other assays present: bin5000, RNA
## 2 dimensional reductions calculated: lsi_, lsi_umap# add the gene information to the object
### Save RDS object:
saveRDS(signac, "H3K27ac_peakMatrixObject_RAW.RDS")
# signac<-
# subset(
# x = signac,subset = passed_filters<75000 &
# FrIP_binClusters > 0.35 &
# promoter_ratio >0.05
# )Binarize
signac[["binaryPeaks"]]<-signac[["peaks"]]## Warning: Cannot add objects with duplicate keys (offending key: peaks_), setting
## key to 'binarypeaks_'signac[["binaryBins"]]<-signac[["bin5000"]]## Warning: Cannot add objects with duplicate keys (offending key: bin5000_),
## setting key to 'binarybins_'signac<- BinarizeCounts(signac, assay=c("binaryPeaks", "binaryBins"))Dimensionality reduction
Find top features from bins-> TF-IDF, SVD, UMAP
DefaultAssay(signac) <- 'peaks'
signac <- FindTopFeatures(signac, min.cutoff = "q5",verbose = T)
signac## An object of class Seurat
## 886758 features across 636 samples within 5 assays
## Active assay: peaks (68396 features, 68396 variable features)
## 4 other assays present: bin5000, RNA, binaryPeaks, binaryBins
## 2 dimensional reductions calculated: lsi_, lsi_umapsignac <- RunTFIDF(signac)## Performing TF-IDF normalization## Warning in RunTFIDF.default(object = GetAssayData(object = object, slot =
## "counts"), : Some features contain 0 total countssignac <- RunSVD(signac,
reduction.key = 'LSI_',
reduction.name = 'lsi_')## Running SVD## Scaling cell embeddingsDepthCor(signac, reduction = "lsi_")
RunUMAP
Excluding dimension #1 since it correlates with seq depth.
signac <- RunUMAP(signac, reduction = 'lsi_', dims = 2:ndim, reduction.name = 'lsi_umap',n.neighbors=15)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 16:08:01 UMAP embedding parameters a = 0.9922 b = 1.112## 16:08:01 Read 636 rows and found 29 numeric columns## 16:08:01 Using Annoy for neighbor search, n_neighbors = 15## 16:08:01 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 16:08:01 Writing NN index file to temp file /tmp/RtmpNLnAOc/filec5ee54cd2f3
## 16:08:01 Searching Annoy index using 30 threads, search_k = 1500
## 16:08:01 Annoy recall = 100%
## 16:08:04 Commencing smooth kNN distance calibration using 30 threads
## 16:08:08 Initializing from normalized Laplacian + noise
## 16:08:08 Commencing optimization for 500 epochs, with 11512 positive edges
## 16:08:12 Optimization finished#signac <- RunUMAP(signac, reduction = 'lsi_', dims = 2:30, reduction.name = 'lsi_umap', n.start=1000,spread=0.24, min_dist=1, n.neighbors = 15)
p_bins<-DimPlot(signac, reduction = 'lsi_umap', label =T, label.box = T, repel = T, group.by = paste0("bin5000_snn_res.",BinRes))+
ggtitle("Clusters from bin5000")+
NoLegend()## Warning: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0.
## Please use `as_label()` or `as_name()` instead.
## This warning is displayed once per session.p_bins
FeaturePlot(signac, features="enhancer_region_ratio" ,min.cutoff = "q5",split.by = "is_cellranger_cell_barcode")
FeaturePlot(signac, features=c("nCount_peaks"),reduction = "lsi_umap" )
FeaturePlot(signac, features=c("FrIP_binClusters"),reduction = "lsi_umap" )
FeaturePlot(signac, signac@assays$peaks@var.features[1:4], slot="counts")
DefaultAssay(signac) <- 'peaks'
signac <- FindNeighbors(object = signac, reduction = 'lsi_', dims =2:ndim, force.recalc = T)## Computing nearest neighbor graph## Computing SNNsignac <- FindClusters(object = signac, verbose = FALSE, algorithm = 3, resolution=PeakRes)## Warning: UNRELIABLE VALUE: Future ('future_lapply-1') unexpectedly generated
## random numbers without specifying argument '[future.]seed'. There is a risk that
## those random numbers are not statistically sound and the overall results might
## be invalid. To fix this, specify argument '[future.]seed', e.g. 'seed=TRUE'.
## This ensures that proper, parallel-safe random numbers are produced via the
## L'Ecuyer-CMRG method. To disable this check, use [future].seed=NULL, or set
## option 'future.rng.onMisuse' to "ignore". # table("binclusters"=paste0("signac@meta.data$bin5000_snn_res.",BinRes),"peak_cluster"=signac$seurat_clusters)
#
p_clusters<-DimPlot(signac, reduction = 'lsi_umap', label =T, label.box = T, repel = T, group.by = "seurat_clusters") +ggtitle("Clusters based on peaks")+NoLegend()
p_clusters
# p_dimplotSamples<-DimPlot(signac, reduction = 'lsi_umap', group.by="hash.ID", label =F, label.box = F, repel = T) +ggtitle("Samples based on peaks")
# p_dimplotSamples
VlnPlot(signac, features="promoter_ratio", split.by="is_cellranger_cell_barcode")+
VlnPlot(signac, features="logUMI", split.by="is_cellranger_cell_barcode")+
plot_layout(guides = "collect")& theme(legend.position = 'bottom')## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.
p_bins+p_clusters+plot_annotation(title="UMAP from peak matrix",theme = theme(plot.title = element_text(hjust = 0.5)))
Make new bam-file for each cluster
cells<-
signac@meta.data %>%
#as_tibble() %>%
rownames_to_column("barcode") %>%
filter(is__cell_barcode=="1") %>%
dplyr::select(barcode, seurat_clusters)
cells$seurat_clusters<-paste0("PeakCluster",cells$seurat_clusters)
write_tsv(x = cells, file=paste0(projPath,"/bam/FilteredCellBarcodes_PeakClusters.tsv"), col_names = FALSE)VlnPlot(signac,
features = c("nCount_bin5000",
"nFeature_bin5000",
"nCount_HTO",
"passed_filters",
#"mitochondrial",
"duplicate",
"enhancerPercentage",
"nCount_RNA",
"nFeature_RNA",
# "HTO_margin",
"blacklist_region_fragments","FrIP_binClusters"
)
)## Warning in FetchData(object = object, vars = features, slot = slot): The
## following requested variables were not found: enhancerPercentage
table(signac$seurat_clusters, signac$is_cellranger_cell_barcode)##
## 0 1
## 0 73 210
## 1 1 186
## 2 3 163markers<-
c(
#"Tbr1",
"Slc17a6","Satb2","Nr5a1","Fezf1",
"Slc32a1","Otp","Dlk1","Adcyap1",
"Agrp", "Pomc", "Npy", "Sst",
"Lepr",
"Meis2",
"Zfhx4",
"Ghrh",
#"Oxt",
#"Cck",
"Ebf3",
#"Gfap",
"Nfib", "Esr1"
#"Rfx4", "Mog", "Alx4", "Gmds"
)
DefaultAssay(signac) <- 'RNA'
p_markers<-FeaturePlot(
object = signac,
features = c(markers, "FrIP_binClusters"),
pt.size = 0.1,
max.cutoff = 'q90',
ncol = round(length(markers)/5)
) & NoLegend() & NoAxes()
p_markers
p_vln_markers<-VlnPlot(signac, features=markers, group.by="seurat_clusters")
p_vln_markers
Save RDS object:
saveRDS(signac,paste0(projPath, "/H3K27ac_PeaksClusteredObject.RDS"))Find DA peaks
DefaultAssay(signac) <- 'peaks'
Idents(signac) <- "seurat_clusters"
da_peaks <- FindAllMarkers(
object = signac,
min.diff.pct = 0.1, #0.005
logfc.threshold = 0.1,
test.use = 'LR'
)## Calculating cluster 0## Calculating cluster 1## Calculating cluster 2#
# da_peaks12<-FindMarkers(signac, ident.1 = "1", ident.2 = "3",
# min.diff.pct = 0.1,
# logfc.threshold = 0.1,
# test.use = 'LR'
# )
da_peaksGR <- makeGRangesFromDataFrame(separate(da_peaks, col=gene, sep="-", c("Chr", "Start", "End")))
elementMetadata(da_peaksGR)<-da_peaks
names(elementMetadata(da_peaksGR))[7]<-"peak"
blacklist.df = read_tsv(file="/projects/jph712/CUTnTag/H3K27ac_ARC_scCUTnTAG_B/deepTools/mm10-blacklist.v3.bed" ,col_names = F) ##
## -- Column specification ----------------------------------------------------------------------------------------------------------------------------------------------
## cols(
## X1 = col_character(),
## X2 = col_double(),
## X3 = col_double()
## )blacklist.gr=GenomicRanges::GRanges(seqnames=blacklist.df$X1,
ranges=IRanges(start=as.numeric(blacklist.df$X2),
end=as.numeric(blacklist.df$X3)))
simpleGR_diff<-GenomicRanges::setdiff(da_peaksGR, blacklist.gr, ignore.strand=TRUE)
da_peaksGR_white<-subsetByOverlaps(da_peaksGR,simpleGR_diff, ignore.strand=TRUE)
filtered_regions.df<-
da_peaksGR_white@elementMetadata %>%
as_tibble() %>%
filter(p_val_adj<0.1 & avg_logFC>0.25 |p_val_adj<0.1 & -0.25>avg_logFC) %>%
distinct(peak, .keep_all=T) %>%
group_by(cluster) %>%
arrange(avg_logFC) %>%
dplyr::select(peak, cluster, avg_logFC,p_val_adj, everything())DefaultAssay(signac) <- 'peaks'
####top in each cluster
closest <- ClosestFeature(signac,
regions = filtered_regions.df$peak,
)
filtered_regions.df<-left_join(filtered_regions.df, closest, by=c("peak"= "query_region"))
filtered_regions.df<-left_join(filtered_regions.df,
as_tibble(binClusters) %>%
unite("peak", c(seqnames, start, end), sep = "-", remove = TRUE)
)## Joining, by = "peak"library(writexl)
writexl::write_xlsx(filtered_regions.df, path = "DiffAcetylatedPeaks_FindAll.xlsx", col_names = TRUE)
########Plot cov plots:
covplot<-CoveragePlot(signac,
filtered_regions.df %>%
group_by(cluster) %>%
slice_min(p_val_adj, n=3) %>%
pull(peak),
extend.upstream = 5000, extend.downstream = 5000,
group.by ="seurat_clusters",peaks = T,assay = "peaks" # not enough - needs to be default assay.
)
suppressWarnings(print(covplot))
TilePlot(
object = signac,
region = "chr2-58289132-58290370", #Acv1r
tile.cells = 100,
group.by = "seurat_clusters",order.by = "total",
extend.upstream = 5000, extend.downstream = 5000
)
goi<- c("chr15-66017872-66018112",
"chr7-141678663-141678913",
"chr3-37350491-37350692",
"chr2-143343751-143344155",
"chr13-51903198-51903570",
"chr13-94856782-94881877"
)
CoveragePlot(signac, region = goi,
group.by = "seurat_clusters",
extend.upstream = 5000, extend.downstream = 5000, ranges = binClusters, peaks=F)#,tile = T, tile.cells = 100)## Warning: Removed 2 rows containing missing values (position_stack).## Warning: Removed 12 rows containing missing values (geom_segment).## Warning: Removed 12 rows containing missing values (geom_rect).## Warning: Removed 18 rows containing missing values (geom_segment).## Warning: Removed 18 rows containing missing values (geom_rect).## Warning: Removed 1 rows containing missing values (geom_segment).## Warning: Removed 1 rows containing missing values (geom_rect).## Warning: Removed 55 rows containing missing values (geom_segment).## Warning: Removed 1 rows containing missing values (geom_text).## Warning: Removed 1 rows containing missing values (position_stack).## Warning: Removed 2 rows containing missing values (position_stack).## Warning: Removed 10 rows containing missing values (geom_segment).## Warning: Removed 10 rows containing missing values (geom_rect).## Warning: Removed 19 rows containing missing values (geom_segment).## Warning: Removed 19 rows containing missing values (geom_rect).## Warning: Removed 5 rows containing missing values (geom_segment).## Warning: Removed 5 rows containing missing values (geom_rect).## Warning: Removed 44 rows containing missing values (geom_segment).## Warning: Removed 1 rows containing missing values (geom_text).## Warning: Removed 1 rows containing missing values (position_stack).
CoveragePlot(signac,
region = "chr3-119195567-119196174",
group.by = "seurat_clusters",
extend.upstream = 5000, extend.downstream = 5000)## Warning: Removed 21 rows containing missing values (geom_segment).## Warning: Removed 21 rows containing missing values (geom_rect).## Warning: Removed 38 rows containing missing values (geom_segment).## Warning: Removed 38 rows containing missing values (geom_rect).## Warning: Removed 2 rows containing missing values (geom_segment).## Warning: Removed 2 rows containing missing values (geom_rect).## Warning: Removed 43 rows containing missing values (geom_segment).## Warning: Removed 1 rows containing missing values (geom_text).
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
##
## locale:
## [1] LC_CTYPE=C LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] writexl_1.3.1
## [2] future_1.20.1
## [3] org.Mm.eg.db_3.12.0
## [4] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [5] GenomicFeatures_1.42.1
## [6] AnnotationDbi_1.52.0
## [7] Biobase_2.50.0
## [8] ChIPseeker_1.26.0
## [9] GenomicRanges_1.42.0
## [10] patchwork_1.1.0
## [11] GenomeInfoDb_1.26.2
## [12] IRanges_2.24.1
## [13] S4Vectors_0.28.1
## [14] BiocGenerics_0.36.0
## [15] Signac_1.1.0
## [16] Seurat_3.2.2
## [17] forcats_0.5.0
## [18] stringr_1.4.0
## [19] dplyr_1.0.2
## [20] purrr_0.3.4
## [21] readr_1.4.0
## [22] tidyr_1.1.2
## [23] tibble_3.0.4
## [24] ggplot2_3.3.2
## [25] tidyverse_1.3.0
## [26] rmarkdown_2.5
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1
## [2] SnowballC_0.7.0
## [3] rtracklayer_1.50.0
## [4] GGally_2.0.0
## [5] bit64_4.0.5
## [6] knitr_1.30
## [7] irlba_2.3.3
## [8] DelayedArray_0.16.0
## [9] data.table_1.13.4
## [10] rpart_4.1-15
## [11] RCurl_1.98-1.2
## [12] AnnotationFilter_1.14.0
## [13] generics_0.1.0
## [14] cowplot_1.1.0
## [15] RSQLite_2.2.1
## [16] shadowtext_0.0.7
## [17] RANN_2.6.1
## [18] bit_4.0.4
## [19] enrichplot_1.10.1
## [20] spatstat.data_1.5-2
## [21] xml2_1.3.2
## [22] lubridate_1.7.9.2
## [23] httpuv_1.5.4
## [24] SummarizedExperiment_1.20.0
## [25] assertthat_0.2.1
## [26] viridis_0.5.1
## [27] xfun_0.19
## [28] hms_0.5.3
## [29] evaluate_0.14
## [30] promises_1.1.1
## [31] fansi_0.4.1
## [32] progress_1.2.2
## [33] caTools_1.18.0
## [34] dbplyr_2.0.0
## [35] readxl_1.3.1
## [36] igraph_1.2.6
## [37] DBI_1.1.0
## [38] htmlwidgets_1.5.2
## [39] reshape_0.8.8
## [40] ellipsis_0.3.1
## [41] RSpectra_0.16-0
## [42] backports_1.2.0
## [43] biomaRt_2.46.0
## [44] deldir_0.2-3
## [45] MatrixGenerics_1.2.0
## [46] vctrs_0.3.5
## [47] ensembldb_2.14.0
## [48] ROCR_1.0-11
## [49] abind_1.4-5
## [50] withr_2.3.0
## [51] ggforce_0.3.2
## [52] BSgenome_1.58.0
## [53] checkmate_2.0.0
## [54] sctransform_0.3.1
## [55] GenomicAlignments_1.26.0
## [56] prettyunits_1.1.1
## [57] goftest_1.2-2
## [58] DOSE_3.16.0
## [59] cluster_2.1.0
## [60] lazyeval_0.2.2
## [61] crayon_1.3.4
## [62] labeling_0.4.2
## [63] pkgconfig_2.0.3
## [64] tweenr_1.0.1
## [65] nlme_3.1-150
## [66] ProtGenerics_1.22.0
## [67] nnet_7.3-14
## [68] rlang_0.4.9
## [69] globals_0.14.0
## [70] lifecycle_0.2.0
## [71] miniUI_0.1.1.1
## [72] BiocFileCache_1.14.0
## [73] modelr_0.1.8
## [74] rsvd_1.0.3
## [75] dichromat_2.0-0
## [76] cellranger_1.1.0
## [77] polyclip_1.10-0
## [78] matrixStats_0.57.0
## [79] lmtest_0.9-38
## [80] graph_1.68.0
## [81] Matrix_1.2-18
## [82] ggseqlogo_0.1
## [83] boot_1.3-25
## [84] zoo_1.8-8
## [85] reprex_0.3.0
## [86] base64enc_0.1-3
## [87] ggridges_0.5.2
## [88] png_0.1-7
## [89] viridisLite_0.3.0
## [90] bitops_1.0-6
## [91] KernSmooth_2.23-18
## [92] Biostrings_2.58.0
## [93] blob_1.2.1
## [94] qvalue_2.22.0
## [95] parallelly_1.21.0
## [96] jpeg_0.1-8.1
## [97] scales_1.1.1
## [98] memoise_1.1.0
## [99] magrittr_2.0.1
## [100] plyr_1.8.6
## [101] ica_1.0-2
## [102] gplots_3.1.1
## [103] zlibbioc_1.36.0
## [104] scatterpie_0.1.5
## [105] compiler_4.0.2
## [106] RColorBrewer_1.1-2
## [107] plotrix_3.7-8
## [108] fitdistrplus_1.1-3
## [109] Rsamtools_2.6.0
## [110] cli_2.2.0
## [111] XVector_0.30.0
## [112] listenv_0.8.0
## [113] pbapply_1.4-3
## [114] ps_1.5.0
## [115] htmlTable_2.1.0
## [116] Formula_1.2-4
## [117] MASS_7.3-53
## [118] mgcv_1.8-33
## [119] tidyselect_1.1.0
## [120] stringi_1.5.3
## [121] yaml_2.2.1
## [122] GOSemSim_2.16.1
## [123] askpass_1.1
## [124] latticeExtra_0.6-29
## [125] ggrepel_0.8.2
## [126] grid_4.0.2
## [127] VariantAnnotation_1.36.0
## [128] fastmatch_1.1-0
## [129] tools_4.0.2
## [130] future.apply_1.6.0
## [131] rstudioapi_0.13
## [132] foreign_0.8-80
## [133] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [134] lsa_0.73.2
## [135] gridExtra_2.3
## [136] farver_2.0.3
## [137] Rtsne_0.15
## [138] ggraph_2.0.4
## [139] rvcheck_0.1.8
## [140] digest_0.6.27
## [141] BiocManager_1.30.10
## [142] shiny_1.5.0
## [143] Rcpp_1.0.5
## [144] broom_0.7.2
## [145] later_1.1.0.1
## [146] RcppAnnoy_0.0.17
## [147] OrganismDbi_1.32.0
## [148] httr_1.4.2
## [149] ggbio_1.38.0
## [150] biovizBase_1.38.0
## [151] colorspace_2.0-0
## [152] rvest_0.3.6
## [153] XML_3.99-0.5
## [154] fs_1.5.0
## [155] tensor_1.5
## [156] reticulate_1.18
## [157] splines_4.0.2
## [158] uwot_0.1.9
## [159] RBGL_1.66.0
## [160] RcppRoll_0.3.0
## [161] spatstat.utils_1.17-0
## [162] graphlayouts_0.7.1
## [163] plotly_4.9.2.1
## [164] xtable_1.8-4
## [165] jsonlite_1.7.2
## [166] tidygraph_1.2.0
## [167] spatstat_1.64-1
## [168] R6_2.5.0
## [169] Hmisc_4.4-2
## [170] pillar_1.4.7
## [171] htmltools_0.5.0
## [172] mime_0.9
## [173] glue_1.4.2
## [174] fastmap_1.0.1
## [175] BiocParallel_1.24.1
## [176] codetools_0.2-18
## [177] fgsea_1.16.0
## [178] lattice_0.20-41
## [179] curl_4.3
## [180] leiden_0.3.6
## [181] gtools_3.8.2
## [182] GO.db_3.12.1
## [183] openssl_1.4.3
## [184] survival_3.2-7
## [185] munsell_0.5.0
## [186] DO.db_2.9
## [187] GenomeInfoDbData_1.2.4
## [188] haven_2.3.1
## [189] reshape2_1.4.4
## [190] gtable_0.3.0